Do computers make mistakes? How does a machine even know what to do? Is Artificial Intelligence really intelligent? This course will guide students through the principles of computer science, exploring the theory and real-world applications of the concepts that govern it. Students will learn about the concepts of algorithms, binary mathematics, Boolean algebra and digital logic, and the theory of computation. They will be introduced to the workings of computer architecture, operating systems, computer networks and embedded systems, and gain insight into the neural networks that power modern AI systems. Throughout the course, the students will have the opportunity to build on their newfound theoretical knowledge through simulations on topics such as Digital Design and Turing Machines, as well as a plethora of hands-on programming challenges, primarily in C++.

Learning Objectives

• Gain a broad understanding of how computing and technology are shaping our world.
• Formulate and implement algorithms in one or more industry-standard programming languages; Investigate code errors, debug and test programs, and evaluate complexity of algorithms
• Think algorithmically to solve programming problems using conditional, iterative and recursive structures, and other techniques.
• Compare and contrast procedural and object-oriented programming paradigms
• Develop collaboration skills in team, project-based learning environments

Did you know that if one person’s DNA was unraveled and placed end to end, it would stretch to the sun and back at least 60 times? Or that humans and chimps share a surprising 98.8 percent of their DNA? How can we be so similar and yet so different? How does all that relate to having your mother’s eyes, or your father’s nose? Or even your great grandmother’s hair? And how did complex, multicellular organisms evolve from simpler, single-celled ones? We begin with an exploration of Mendelian genetics to determine how simple traits are passed from parents to offspring, delve into more complex concepts such as sex-linked traits and polygenic inheritance, to move towards understanding the genetics of inherited disorders. We will also take a look into the fascinating world of 6 million years of evolution. Furthermore, we learn and practice some of the methods and techniques that geneticists use to explore these concepts, such as PCR, gel electrophoresis, and bacterial transformations. 

Learning objectives

• Predict the impact of mutations and the inheritance patterns of different diseases.
• Utilize biotechnological laboratory skills to determine the genotypes of individuals and explore the process of transformation, a key technique in genetic engineering. 
• Research and present a genetically inherited disease/syndrome including characteristics such as genetic heterogeneity, penetrance and expressivity.

Chance plays an important part in all aspects of life.

We take chances every day: will a shot at goal land in the goal or miss? Will we be caught in a sudden shower or not? How long do we need to wait to be served in our favourite burger house?

Chance or random variation is also a central feature of all working systems: a scientist taking measurements in a lab; a disease spreading through a population; an economist studying price fluctuation. In all these processes some element of chance or randomness are present.  Is it possible to understand and therefore model and analyse such phenomena? If so, what are the tools we need to achieve that? Do we live in a world of randomness, or, as Einstein famously claimed, no one plays dice with the universe?

During this course, we will attempt to “tame randomness” using mathematics as our compass. 

Learning objectives:

• Develop a robust theoretical understanding of the basics of probability theory. 
• Develop the capability to identify the underlying randomness in real life problems, and decide how to model and quantify it.
• Gain an in-depth understanding of the basic technical tools needed in applied probability.
• Make use of random variables and theoretical probability distributions to model simple random processes (Η).